def UpdateContribution(self):
if not self.checkContributionShow.isChecked():
return
if self.radioContributionFeatureSelector.isChecked():
file_name = self.comboContributionFeatureSelector.currentText() + '_sort.csv'
file_path = self._SearchSpecificFile(file_name, int(self._fae.GetFeatureNumberList()[0]))
if file_path:
df = pd.read_csv(file_path, index_col=0)
feature_name = list(df.index)
value = list(np.abs(df.iloc[:, 0]))
GeneralFeatureSort(feature_name, value, max_num=self.spinFeatureSelectorFeatureNumber.value(),
is_show=False, fig=self.canvasFeature.getFigure())
elif self.radioContributionClassifier.isChecked():
specific_name = self.comboContributionClassifier.currentText() + '_coef.csv'
file_path = self._SearchSpecificFile(specific_name, self.spinClassifierFeatureNumber.value())
if file_path:
df = pd.read_csv(file_path, index_col=0)
feature_name = list(df.index)
value = list(np.abs(df.iloc[:, 0]))
try:
SortRadiomicsFeature(feature_name, value, is_show=False, fig=self.canvasFeature.getFigure())
except:
GeneralFeatureSort(feature_name, value,
is_show=False, fig=self.canvasFeature.getFigure())
self.canvasFeature.draw()
def UpdateContribution(self):
if not self.checkContributionShow.isChecked():
return
if self.radioContributionFeatureSelector.isChecked():
file_name = self.comboContributionFeatureSelector.currentText() + '_sort.csv'
file_path = self._SearchSpecificFile(int(self._fae.GetFeatureNumberList()[0]), file_name)
if file_path:
df = pd.read_csv(file_path, index_col=0)
feature_name = list(df.index)
value = list(np.abs(df.iloc[:, 0]))
#add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[index] = processed_feature_name[index] + 'P'
else:
processed_feature_name[index] = processed_feature_name[index] + 'N'
GeneralFeatureSort(processed_feature_name, value, max_num=self.spinFeatureSelectorFeatureNumber.value(),
is_show=False, fig=self.canvasFeature.getFigure())
elif self.radioContributionClassifier.isChecked():
specific_name = self.comboContributionClassifier.currentText() + '_coef.csv'
feature_selector_name = self.comboContributionFeatureSelector.currentText()
file_path = self._SearchSpecificFile(self.spinClassifierFeatureNumber.value(), specific_name, feature_selector_name)
if file_path:
df = pd.read_csv(file_path, index_col=0)
feature_name = list(df.index)
value = list(np.abs(df.iloc[:, 0]))
#add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[index] = processed_feature_name[index] + ' P'
else:
processed_feature_name[index] = processed_feature_name[index] + ' N'
try:
SortRadiomicsFeature(processed_feature_name, value, is_show=False, fig=self.canvasFeature.getFigure())
except:
GeneralFeatureSort(processed_feature_name, value,
is_show=False, fig=self.canvasFeature.getFigure())
self.canvasFeature.draw()
def UpdateContribution(self):
if (not self.__is_ui_ready) or self.__is_clear:
return
try:
pipeline_name = self._fae.GetStoreName(
self.comboContributionNormalizor.currentText(),
self.comboContributionDimension.currentText(),
self.comboContributionFeatureSelector.currentText(),
str(self.spinContributeFeatureNumber.value()),
self.comboContributionClassifier.currentText())
norm_folder, dr_folder, fs_folder, cls_folder = self._fae.SplitFolder(
pipeline_name, self._root_folder)
if self.radioContributionFeatureSelector.isChecked():
file_name = self.comboContributionFeatureSelector.currentText(
) + '_sort.csv'
file_path = os.path.join(fs_folder, file_name)
if file_path:
df = pd.read_csv(file_path, index_col=0)
value = list(df.iloc[:, 0])
sort_by = df.columns.values[0]
if sort_by == 'rank':
reverse = False
elif sort_by == 'F' or sort_by == 'weight':
reverse = True
else:
reverse = False
print('Invalid feature selector sort name.')
# add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[
index] = processed_feature_name[index] + ' P'
else:
processed_feature_name[
index] = processed_feature_name[index] + ' N'
GeneralFeatureSort(
processed_feature_name,
value,
max_num=self.spinContributeFeatureNumber.value(),
is_show=False,
fig=self.canvasFeature.getFigure(),
reverse=reverse)
elif self.radioContributionClassifier.isChecked():
specific_name = self.comboContributionClassifier.currentText(
) + '_coef.csv'
file_path = os.path.join(cls_folder, specific_name)
if os.path.exists(file_path):
df = pd.read_csv(file_path, index_col=0)
value = list(np.abs(df.iloc[:, 0]))
# add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[
index] = processed_feature_name[index] + ' P'
else:
processed_feature_name[
index] = processed_feature_name[index] + ' N'
GeneralFeatureSort(processed_feature_name,
value,
is_show=False,
fig=self.canvasFeature.getFigure())
self.canvasFeature.draw()
except Exception as e:
content = 'In Visualization, UpdateContribution failed'
self.logger.error('{}{}'.format(content, str(e)))
QMessageBox.about(self, content, e.__str__())
def UpdateContribution(self):
if not self.__is_ui_ready:
return
try:
one_result_folder_name = self.comboContributionNormalizor.currentText() + '_' + \
self.comboContributionDimension.currentText() + '_' + \
self.comboContributionFeatureSelector.currentText() + '_' + \
str(self.spinContributeFeatureNumber.value()) + '_' + \
self.comboContributionClassifier.currentText()
one_result_folder = os.path.join(self._root_folder, one_result_folder_name)
# This is compatible with the previous version
if not os.path.exists(one_result_folder):
one_result_folder_name = self.comboContributionNormalizor.currentText() + '_Cos_' + \
self.comboContributionFeatureSelector.currentText() + '_' + \
str(self.spinContributeFeatureNumber.value()) + '_' + \
self.comboContributionClassifier.currentText()
one_result_folder = os.path.join(self._root_folder, one_result_folder_name)
if self.radioContributionFeatureSelector.isChecked():
file_name = self.comboContributionFeatureSelector.currentText() + '_sort.csv'
file_path = os.path.join(one_result_folder, file_name)
if not os.path.exists(file_path):
file_name = self.comboContributionFeatureSelector.currentText().lower() + '_sort.csv'
file_path = os.path.join(one_result_folder, file_name)
if file_path:
df = pd.read_csv(file_path, index_col=0)
value = list(df.iloc[:, 0])
#add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[index] = processed_feature_name[index] + ' P'
else:
processed_feature_name[index] = processed_feature_name[index] + ' N'
GeneralFeatureSort(processed_feature_name, value, max_num=self.spinContributeFeatureNumber.value(),
is_show=False, fig=self.canvasFeature.getFigure())
elif self.radioContributionClassifier.isChecked():
specific_name = self.comboContributionClassifier.currentText() + '_coef.csv'
file_path = os.path.join(one_result_folder, specific_name)
if not os.path.exists(file_path):
specific_name = self.comboContributionClassifier.currentText().lower() + '_coef.csv'
file_path = os.path.join(one_result_folder, specific_name)
if file_path:
df = pd.read_csv(file_path, index_col=0)
feature_name = list(df.index)
value = list(np.abs(df.iloc[:, 0]))
#add positive and negatiove info for coef
processed_feature_name = list(df.index)
original_value = list(df.iloc[:, 0])
for index in range(len(original_value)):
if original_value[index] > 0:
processed_feature_name[index] = processed_feature_name[index] + ' P'
else:
processed_feature_name[index] = processed_feature_name[index] + ' N'
# try:
# SortRadiomicsFeature(processed_feature_name, value, is_show=False, fig=self.canvasFeature.getFigure())
# except:
GeneralFeatureSort(processed_feature_name, value,
is_show=False, fig=self.canvasFeature.getFigure())
self.canvasFeature.draw()
except Exception as e:
content = 'In Visualization, UpdateContribution failed'
self.logger.error('{}{}'.format(content, str(e)))
QMessageBox.about(self, content, e.__str__())